Wavelet/scalar quantization compression standard for fingerprint images

US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class of potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations

Map online system using internet-based image catalogue

Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented

A Vlsi architecture for lifting-based wavelet packet transform in fingerprint image compression

FBI uses a technique called Wavelet Scalar Quantization (WSQ), a wavelet packet transform (WPT) based method, to compress its fingerprint images. Though many VLSI architectures have been proposed for wavelet transform in the literature, it is not the case for the WPT. In this thesis, a VLSI architecture capable of computing the WPT is presented for application of WSQ. In the proposed architecture, Lifting Scheme (LS) is used to generate wavelets instead of the traditional convolution filter-bank (FB) specified in original standard. A comparative study between LS and FB shows that quality of images transformed by LS is completely acceptable (with 30dB∼40dB PSNR at a target bit rate of 0.75dpp) while fewer operations required. In particular, to compare with FB, the hardware consumption, for our WSQ application, is reduced to half due to the LS. Moreover, this architecture can be easily configured to compute any required WPT application

Fingerprint Image Compression Using Wavelet Transform

The fingerprint is considered to be the most reliable kind of personal identification because it cannot be forgotten, misplaced, or stolen. Fingerprint authorization is potentially the most affordable and convenient method of verifying a person's identity. Storage of fingerprint image databases needs allocation of huge secondary storage devices. To reduce the increasing demand on storage space, efficient data compression techniques are needed. In addition to that, the exchange of fingerprint images between the governmental agencies could be done fast. The compression algorithm must also preserve original information in the original image. Digital image compression based on the ideas of subband decomposition or discrete wavelet transform (DWT) has received much attention in recent years. In fact, wavelet refers to a set of basic function, which is recursively defined form, a set of scaling coefficients and scaling function. Discrete Wavelet Transform CDWT) represents images as a sum of wavelet function on different resolution level. Essential for wavelet transform can be composed of any function that satisfies requirements of multi-resolution analysis. It means that there exists a large selection of wavelet families depending on choice of wavelet function. The objective of this study is to evaluate a variety of wavelet filters using Wavelet toolbox for selecting the best wavelet filters to be used in compress and decompress of selected fingerprint images. Therefore a two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks were applied to a set of fingerprint images. The results show that no specific wavelet filter performs uniformly except for Biorthogonal and Symlets, and that is using the matching technique. The result shows that at a threshold value equal of 160 and decomposition level 3 with a wavelet filter sym4, there is no difference between the original and reconstructed image. This study concludes that using wavelet filters sym4 and bior3.7 can achieve compression ratio 27: 1 with PSNR 20.36 dB and 17: 1 with PSNR 21.88 dB respectively. These values indicate that using these filters, the quality of the reconstructed fingerprint still exist

A Model-Based Approach for Compression of Fingerprint Images

We propose a new fingerprint image compression scheme based on the hybrid model of an image. Our scheme uses the essential steps of a typical automated fingerprint identification system (AFIS) such as enhancement, binarization and thinning to encode fingerprint images. The decoding process is based on reconstructing a hybrid surface by using the gray values on ridges and valleys. In this compression scheme, the ridge skeleton is coded efficiently by using differential chain codes. The valley skeleton is derived from the ridge skeleton and the gray values along the ridge and valley skeletons are encoded using the discrete cosine transform. The error between the original and the replica is also encoded to increase the quality. One advantage of our approach is that original features such as end points and bifurcation points can be extracted directly from compressed image even for a very high compression ratio. Another advantage is that the proposed scheme can be integrated to a typical AFIS easily. The algorithm has been applied to various fingerprint images, and high compression ratios like 63:1 have been obtained. A comparison to wavelet/scalar quantization (WSQ) has been also made